1. Performance Measuring on Blue Horizon and Sun HPC Systems: Timing, Profiling, and Reading Assembly Language NPACI Parallel Computing Institute 2000 Sean Peisert peisert@sdsc.edu

2. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 2 Purpose Applications, as they are first written, can be initially very slow. Sometimes, even the most well-planned code can be made to run one or more orders of magnitude faster. To speed up applications, one must understand what is happening in the application.

3. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 3 Techniques By timing code, one can understand how fast or slow an application is running but not how fast it can potentially run. By profiling code, one can understand where the application is taking the most time. By reading assembly language, one can understand if the sections that the profiler identifies as slow are acceptable or poorly compiled.

4. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 4 Benefits By tuning code in a knowledgeable way, one can often significantly speed up an application. Using the techniques of timing, profiling, and reading assembly language, one can make educated guesses about what to do instead of shooting blindly.

5. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 5 Timing Terms Code for a single node: –Wallclock time –CPU time Code for a parallel machine: –Computation time –Communication time Latency Bandwidth

6. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 6 Timing on Parallel Machines Latency is the time it takes to send a message from one processor to another. Bandwidth is the amount of data in a given time period that can be sent from one processor to another. [communication time] = [startup time] + [message size]/[bandwidth]

7. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 7 Timing Latency Different machines might be suited for coarse or fine-grained communication. The Sun HPC system and Blue Horizon both do fairly well intra-node, but inter-node communication is slower. Run ‘ring’ benchmarks to time communication latency.

8. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 8 Ring Pass messages from one processor to the next and back to the first in a ring fashion. Have it do multiple cycles (it has to warm up). Increase the size of the message passed until the time to pass it stabilizes. It will help to characterize the performance of message-passing to determine how large the messages in a “real” program can/should be.

9. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 9 Timing on Parallel Machines Tips Make sure that the system clocks on all machines are the same. In addition to the time for communication and computation, there is also “waiting.” Remember that some forms of communication (i.e. MPI_Recv()) are “blocking.” Goal is to minimize waiting and communication relative to computation.

10. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 10 Performance Measuring with Timings: Wallclock Wallclock time (real time, elapsed time) –High resolution (unit is typically 1 µs) –Best to run on dedicated machines –Good for inner loops in programs or I/O. –First run may be varied due to acquiring page frames.

11. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 11 Performance Measuring with Timings: CPU CPU time –User Time: instructions, cache, & TLB misses –System time: initiating I/O & paging, exceptions, memory allocation –Low resolution (typically 1/100 second) –Good for whole programs or a shared system.

12. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 12 Timing Tips Wallclock time contains everything that CPU time contains but it also includes waiting for I/O, communication, and other jobs. For any timing results use several runs (three or more) and use the minimum time, not the average times.

13. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 13 Wallclock Time gettimeofday() — C/C++ –Resolution up to microseconds. MPI_Wtime() — C/C++/Fortran Others: ftime, rtc, gettimer,... Both Blue Horizon and “gaos” (Sun HPC) have gettimeofday(), MPI_Wtime(), and ftime.

14. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 14 gettimeofday() C++ Example #include struct timeval *Tps, *Tpf; void *Tzp; Tps = (struct timeval*) malloc(sizeof(struct timeval)); Tpf = (struct timeval*) malloc(sizeof(struct timeval)); Tzp = 0; gettimeofday (Tps, Tzp); gettimeofday (Tpf, Tzp); printf("Total Time (usec): %ld\n", (Tpf->tv_sec-Tps->tv_sec)*1000000 + Tpf->tv_usec-Tps->tv_usec);

15. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 15 MPI_Wtime() C++ Example #include double start, finish; start = MPI_Wtime(); finish = MPI_Wtime(); printf(“Final Time: %f”, finish-start); /* Time is in milliseconds since a particular date */

16. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 16 CPU Timing For timing the entire execution, use UNIX ‘time’ –Gives user, system and wallclock times. For timing segments of code: ANSI C #include Clock_t is type of CPU times clock()/CLOCKS_PER_SEC

17. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 17 CPU Timing SYSTEM_CLOCK() — Fortran (77, 90) –Resolution up to microseconds

18. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 18 SYSTEM_CLOCK() INTEGER TICK, STARTTIME, STOPTIME, TIME CALL SYSTEM_CLOCK(COUNT_RATE = TICK)... CALL SYSTEM_CLOCK (COUNT = STARTTIME) CALL SYSTEM_CLOCK (COUNT = STOPTIME) TIME = REAL(STOPTIME-STARTTIME) / REAL(TICK) PRINT 4, STARTTIME, STOPTIME, TICK 4 FORMAT (3I10)

19. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 19 Example time Output 5.250u 0.470s 0:06.36 89.9% 7787+30041k 0+0io 805pf+0w 1st column = user time 2nd column = system time 3rd column = total time 4th column = (user time + system time)/total time in %. In other words, the percentage of time your job gets alone. 5th column = (possibly) memory usage 7th column = page faults

20. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 20 time Tips Might need to specifically call /usr/bin/time instead of the built-in time. Look for low “system” time. A significant system time may indicate many exceptions or other abnormal behavior that should be corrected.

21. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 21 More About Timing Compute times in cycles/iteration and compare to plausible estimate based on the assembly instructions. (([program time]-[initialization time]) * [clock speed in Hz])/[number of cycles]

22. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 22 More About Timing Compute time of the program using only a single iteration to determine how many seconds of timing, loop, and execution overhead are present in every run. Subtract the overhead time from each run when computing cycles/iteration. Make sure that the system clock on each machine is the same time.

23. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 23 Profiling – Where does the time go? Technique using xlc compiler for an executable called ‘a.out’: Compile and link using ‘-pg’ flag. Run a.out. The executable produces the file ‘gmon.out’ in the same directory. Run several times and rename ‘gmon.out’ to ‘gmon.1, gmon.2, etc…’ Execute: ‘gprof a.out gmon.1 gmon.2 > profile.txt’

24. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 24 Profiling: gprof output Output may look like this: % cumulative self self total time seconds seconds calls ms/call ms/call name 72.5 8.10 8.10 160 50.62 50.62.snswp3d [3] 7.9 8.98 0.88 __vrec [9] 6.2 9.67 0.69 160 4.31 7.19.snnext [8] 4.1 10.13 0.46 160 2.88 2.88.snneed [10] 3.1 10.48 0.35 2 175.00 175.00.initialize [11] 1.8 10.68 0.20 2 100.00 700.00.rtmain [7] 1.5 10.85 0.17 8 21.25 1055.00.snflwxyz@OL@1 0.7 10.93 0.08 320 0.25 0.25.snxyzbc [12]

25. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 25 Profiling Techniques Look for the routing taking the largest percentage of the time. That is the routine, most possibly, to optimize first. Optimize the routine and re-profile to determine the success of the optimization. Tools on other machines: prof, gvprof, apprentice, prism.

26. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 26 Assembly Code Being able to read assembly code is critical to understanding what a program is doing. Writing assembly code is often unnecessary, however. To get useful assembly code on Blue Horizon, compile with the “-qsource” and - qlist” options. After being compiled, the output gets put in a “.lst” file.

27. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 27 Reading.lst Files At the top of the file, there is a list of line numbers. Find the line number(s) of the inner loop(s) of your program, then scroll down to where those lines appear (in the leftmost column). If you are using timers around your inner loop, it will usually be between the timing statements.

28. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 28 Don’t Panic! There are a few commands that one wants to learn. They appear in the third column and they describe what the program is doing. If there are “unnecessary commands,” the program is wasting time. Additionally, there are “predicted” numbers of cycles in the fifth column. Determining how well these match up with the actual number of cycles per iteration is very useful.

29. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 29 Basic PowerPC Commands fadd = floating-point add subf = floating-point subtract lfd = load double word lwz = load integer word stw = store integer word bc = branch on count addi = add immediate ori = or immediate

30. NPACI Parallel Computing Institute 2000 — Timing, Profiling and Reading Assembly Language — Sean Peisert 30 More Information PRISM Documentation: http://docs.sun.com:80/ab2/coll.514.2/PRISMUG/ Parallel Communication Benchmarks: http://www.cse.ucsd.edu/users/baden/cse268a/PA/p a1.htm Timer Documentation: man [gprof, prism, MPI_Wtime, etc...]